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TGI LLM Microservice

Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more.

🚀1. Start Microservice with Python (Option 1)

To start the LLM microservice, you need to install python packages first.

1.1 Install Requirements

pip install -r requirements.txt

1.2 Start 3rd-party TGI Service

Please refer to 3rd-party TGI to start a LLM endpoint and verify.

1.3 Start LLM Service with Python Script

export TGI_LLM_ENDPOINT="http://${your_ip}:8008"
python llm.py

🚀2. Start Microservice with Docker (Option 2)

If you start an LLM microservice with docker, the docker_compose_llm.yaml file will automatically start a TGI/vLLM service with docker.

2.1 Setup Environment Variables

In order to start TGI and LLM services, you need to setup the following environment variables first.

export HF_TOKEN=${your_hf_api_token}
export TGI_LLM_ENDPOINT="http://${your_ip}:8008"
export LLM_MODEL_ID=${your_hf_llm_model}

2.2 Build Docker Image

cd ../../../../
docker build -t opea/llm-textgen:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/src/text-generation/Dockerfile .

To start a docker container, you have two options:

  • A. Run Docker with CLI
  • B. Run Docker with Docker Compose

You can choose one as needed.

2.3 Run Docker with CLI (Option A)

docker run -d --name="llm-tgi-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TGI_LLM_ENDPOINT=$TGI_LLM_ENDPOINT -e HF_TOKEN=$HF_TOKEN opea/llm-textgen:latest

2.4 Run Docker with Docker Compose (Option B)

cd comps/llms/deployment/docker_compose/
docker compose -f text-generation_tgi.yaml up -d

🚀3. Consume LLM Service

3.1 Check Service Status

curl http://${your_ip}:9000/v1/health_check\
  -X GET \
  -H 'Content-Type: application/json'

3.2 Consume LLM Service

You can set the following model parameters according to your actual needs, such as max_tokens, stream.

The stream parameter determines the format of the data returned by the API. It will return text string with stream=false, return text stream flow with stream=true.

# stream mode
curl http://${your_ip}:9000/v1/chat/completions \
    -X POST \
    -d '{"model": "${LLM_MODEL_ID}", "messages": "What is Deep Learning?", "max_tokens":17}' \
    -H 'Content-Type: application/json'

curl http://${your_ip}:9000/v1/chat/completions \
    -X POST \
    -d '{"model": "${LLM_MODEL_ID}", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
    -H 'Content-Type: application/json'

#Non-stream mode
curl http://${your_ip}:9000/v1/chat/completions \
    -X POST \
    -d '{"model": "${LLM_MODEL_ID}", "messages": "What is Deep Learning?", "max_tokens":17, "stream":false}' \
    -H 'Content-Type: application/json'

For parameters in Chat mode, please refer to OpenAI API

4. Validated Model

Model TGI
Intel/neural-chat-7b-v3-3 ✓
Llama-2-7b-chat-hf ✓
Llama-2-70b-chat-hf ✓
Meta-Llama-3-8B-Instruct ✓
Meta-Llama-3-70B-Instruct ✓
Phi-3 ✓